Design and analysis of longitudinal–flexural hybrid transducer for ultrasonic peen forming

Ultrasonic peen forming(UPF)is an emerging technology that exhibits great superiority in both its flexible operating modes and the deep residual stress that it produces compared with conventional plastic forming methods.Although ultrasonic transducers with longitudinal vibration have been widely studied,they have seldom been incorporated into UPF devices for machining in confined spaces.To meet the requirements of this type of machining,a sandwich-type piezoelectric transducer with coupled longitudinal-flexural vibrational modes is proposed.The basic structure of the transducer is designed to obtain large vibrational amplitudes in both modes.Experimental results obtained with a prototype device demonstrate the feasibility of the proposed transducer.The measured vibrational amplitude for the working face in the longitudinal vibrational mode is 1.0μm,and electrical matching increases this amplitude by 40%.The flexural vibration characteristics of the same prototype transducer are also tested and are found to be slightly smaller than those of longitudinal mode.The resultant working strokes of the UPF impact pins reach 1.7 mm and 1.2 mm in the longitudinal and flexural modes,respectively.The forming capability of the prototype has been evaluated via 15-min machining on standard 2024-T351 aluminum plates.After UPF,an improved surface morphology with lower surface roughness is obtained.The aluminum plate test piece has an apparent upper deformation with an arc height of 0.64 mm.The measured peak value of the compressive residual stress is around 250 MPa,appearing at a depth of 100μm.The proposed longitudinal-flexural hybrid transducer thus provides a high-performance tool for plate peen forming in confined spaces.

Process-based deep learning model:3D prediction method for shot peen forming of an aircraft panel

Shot peen-forming is a more precise method of forming aircraft panels than conventional methods.The traditional method of acquiring the process parameters relies mainly on prior theoretical knowledge and trial-and-error.Despite the finite element method’s ability to replace some experimentation,it still cannot realize the design of shot peen forming processes parameters of an aircraft panel based on a known contour.This study uses an innovative model-based deep learning approach to predict aircraft panel deformation and active design the shot peening parameters.The prediction time is less than 1 second,resulting in a significant reduction in computational time.The shot peen forming process parameters and the geometric structure characteristics of the aircraft panel are divided into independent channels to establish a high-dimensional feature map,which are used to train the deep learning model.The forming contours of the 2024-T351 high-strength aluminum alloy panel are predicted under different shot peening processes.In addition,the process parameters are designed according to the known contour of the forming process.To verify the precision of the proposed method,the designed shot peen forming process is used to manufacture a single curvature aircraft panel with a curvature radius of 3500 mm.There is good agreement between the forming contour and the theoretical design contour.The maximum deformation error is less than 1 mm and its mean error is 7.8%.The mean curvature radius error is 5.668%.The proposed method provides a new and practical reference to the precise design of the shot peen-forming process.